Issue 68

S. H. Moghtaderi et alii, Frattura ed Integrità Strutturale, 68 (2024) 197-208; DOI: 10.3221/IGF-ESIS.68.13

Figure 6: Comparison of maximum normal stress in y-direction of SIF model and FEM for different amount of crack length.

In addition, Fig 7 investigates the effect of crack length on the maximum stress component in the y-direction estimated from the SIF model over a range of characteristic lengths (0 < ξ ≤ 1 mm). Notably, as illustrated in Fig 7, reaching the exact crack tip ( ξ = 0) is impractical due to stress concentration, which results in an infinite maximum stress value. The simulation data for the four distinct crack lengths (0.1, 1.0, 5.0, and 10 mm) are depicted on the relevant crack length curves. Assuming implementing simulation iteration II, for example, the red curve displaying a crack length of 5.0 mm corresponds to a maximum stress value of 4.5 MPa when the characteristic length is 0.16 mm, matching the SIF model result.

Figure 7: Comparison of maximum normal stress in y-direction of SIF model and FEM for different amount of characteristic length.

The outputs of the ANN model are discussed as well here. Tab. 5 provides the data that was elected as test data for ANN prediction. In this study, 10% of the total dataset obtained from simulations has been set apart for testing, guaranteeing that these specific data points were not exposed to the ANN model during the training phase. The results of the ANN for this specific testing dataset are considered predictions from the machine learning model. The procedure is illustrated in Tab. 5, where the ANN model predicts data that has not before been seen during the training phase. For instance, the ANN model predicts a result of 1.18 MPa when the FEM yields 1.15 MPa for a crack length of 1 mm in the first simulation iteration, demonstrating high accuracy of machine learning model. An important part of assessing the performance of machine learning model is the selected test data. As these test data points are distinct from the training dataset, the model is subjected to the circumstances that it was not trained on. This deliberate omission benefits to evaluate the capacity of the model for generalization and precise prediction-making on fresh, untested data. By essentially subjecting the machine learning model to various situations, it is possible to evaluate its ability to predict and obtain insight into its general reliability and efficacy.

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